1. Field of the Invention
The present invention relates generally to classifiers to of objects embedded in received advanced sensor signals and more particularly to radar classifiers for recognizing targets obscured by noise, scintillation, and clutter occurring in returned radar signals.
2. Discussion of Related Art
Many technologies share a common problem: data, as first received by a sensor, is typically difficult to interpret in its received form and must be processed using transforms and filters. The processing renders a new representation that lays bare salient features in order to allow classification of application-important objects. Often these transforms involve expanding the sensor data onto an analysis basis of functions spanning Hilbert space. For example, in pulse Doppler radar, a moving target that is obscured by ground clutter may be separated from ground clutter by performing a Fourier transform. The transform shifts in frequency the target out of the portion of the Doppler spectrum occupied by the clutter and into a clutter-clear region. The target may then be extracted by appropriate filtering in the Fourier domain. The filter number of the Fourier filter containing the target energy is then an indication of the target velocity, and thus partial classification information.
Use of the Fourier transform effects a supposition that the sensor data may be aptly represented as a superposition of sinusoids. In many sensor applications (e.g., pulse Doppler radar) the use of sinusoids is fruitful; however, in numerous other important instances the Fourier representation is cumbersome (e.g., coefficient-intensive when representing sharp features) or fails altogether to yield an interpretable representation (e.g., resolution of multiple superposed FM signals). Moreover, the Fourier method does not exploit the opportunity to allow a priori information about sensor and target to figure in the design of the analysis basis for the transform. Often the impulse response of the data, the nature of the target and its location and other physics of the sensor target classification problem are known. Ignoring this known information when selecting an analysis basis for the transform, and perfunctorily using the Fourier method, often results in complex and/or impenetrable sensor data representations.
Many objects of interest for classification occurring in sensor data have attribute or compact support; they have short duration (i.e., non-stationary signals). Prior art has attempted to exploit the compact support in such applications by applying a compact window to the Fourier kernel to implement a windowed Fourier transform. While useful for certain applications, typically, these windowed Fourier transforms are not reversible transforms. As a result, inadvertent loss of information and/or introduction of artifacts may occur, which may be pernicious for classification applications.